POMFRET
Description
**Project Title:** Good and Bad Classification of Pomfret Fish (Pampus argenteus) Using Samsung F62 Mobile Camera **Description:** The project "Good and Bad Classification of Pomfret Fish (Pampus argenteus)" aims to develop an automated system for distinguishing between healthy (good) and unhealthy (bad) pomfret fish using image classification techniques. The dataset consists of over 600 images, equally divided between good and bad samples. All images were captured using a Samsung F62 mobile camera against a black background in daylight conditions, ensuring consistent and high-quality visual data. **Dataset Composition:** - **Good Samples (Healthy):** The dataset includes over 300 images of healthy pomfret fish, exhibiting normal body structure, clear eyes, and vibrant coloration. These images serve as positive samples for training the classification model. - **Bad Samples (Unhealthy):** The dataset also contains over 300 images of unhealthy pomfret fish, showing signs of disease, physical damage, discoloration, or other factors indicating poor health. These images represent the negative class for model training and evaluation. **Data Collection Setup:** All images were captured using the Samsung F62 mobile camera to ensure high resolution and consistent image quality. The use of a black background helps to highlight the fish's features and reduce environmental distractions, while daylight conditions provide natural lighting, enhancing the visibility of details necessary for accurate classification. **Image Characteristics:** The dataset includes images of pomfret fish with various physical conditions and features. This diversity ensures that the classification model can generalize well and accurately identify the health status of pomfret fish under different conditions. **Data Annotation:** Each image is carefully annotated to indicate whether the fish is in a good or bad condition. These annotations serve as the ground truth, essential for training, validating, and testing the machine learning model. **Data Preprocessing:** Preprocessing steps include resizing images to a standard resolution, normalizing pixel values, and performing data augmentation techniques such as rotation, flipping, and scaling to enhance the model's robustness. These preprocessing steps help the model learn relevant features and improve its performance on unseen data. **Applications:** - **Fisheries Management:** The classification system can assist in monitoring the health of pomfret fish populations, enabling timely interventions to maintain fish health and quality. - **Seafood Quality Control:** In seafood markets, the model can help in ensuring that only healthy fish are sold to consumers, improving food safety and customer satisfaction. - **Research:** The dataset and model can be used in research to further explore the health indicators of pomfret fish and improve classification techniques.